Signal averaging is known, and is often employed in the measurement of experimental signals. More particularly, signal averaging is generally used to measure weak response signals to a repetitive stimulus. Such response signals often have a magnitude that is low relative to an accompanying background noise level, making accurate measurement of such response signals difficult. Signal averaging is used to increase the signal-to-noise ratio of such response signals.
Signal averaging is generally performed by measuring the response signals repeatedly in succession, adding all of the response signals measured, and dividing by the number of repeated measurements. Signal averaging is based on the principle that the background noise is usually uncorrelated from measurement to measurement (i.e., random) and will therefore gradually cancel out, while the desired response signal is repeatable and will therefore continue to add up. Signal averaging, as such, is therefore used to reveal response signals that are buried in the background noise, and would thus be otherwise undetectable.
Many applications currently use signal averaging to improve the signal-to-noise ratio of desired signals. Signal averaging is used in many medical procedures, such as, for example, electrocardiography (“EKG”), electroencephalography (“EEG”), magnetic resonance imaging (“MRI”), brainstem auditory evoked response (“BAER”) testing, transient evoked otoacoustic emissions (TEOAE) testing, distortion product otoacoustic emissions (“DPOAE”) testing, and ultrasound imaging. Signal averaging is also used in many non-medical applications, such as, for example, ultrasound imaging analysis of various materials and their properties, global positioning systems (“GPS”), radio detecting and ranging (“RADAR”), various types of spectroscopy, and communications.
Most of the above applications are performed in environments having considerable background noise present. For example, medical procedures are performed in rooms having fluorescent light ballasts, power supplies, sensors, heaters, computer equipment, etc., all of which contribute to the background noise. In addition, for applications measuring stimulus-evoked responses, such as, for example, BAER, TEOAE and DPOAE testing, the ongoing background activity of the brain may also contribute to the background noise. In fact, the stimulus itself may produce an artifact that obscures the response signal of interest. Thus, in any given application, the background noise can vary considerably, causing variations in the overall measured magnitude.
One traditional signal averaging approach to combat such problems is to generate a noise floor threshold, reject the values measured above the threshold as noise, and accept those below. In application, this approach involves measuring the response, calculating the noise floor, and then adding the response to an average buffer only if the noise floor is below a threshold value. The number of responses added to the buffer is counted, and only those signals are used in the averaging calculation.
However, an optimal threshold cannot be determined until after the test is completed. A simple approach to generate an optimal threshold, therefore, is to wait until the test is completed. In other words, the data is recorded first, and then used to determine the most optimal noise floor threshold. Such approach, however, requires that the complete response must be stored, making such approach often impractical due to memory and battery limitations. This is particularly true in applications that use hand-held test devices, such as, for example, DPOAE testing, where memory storage is limited and battery drain is a critical operating factor. In addition, such approach is impractical in applications where real-time measurements are desired, particularly in the medical field.
It is therefore an object of the invention to provide an improved and practical signal averaging approach for many applications.